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orpe42
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deberta_MP_dynamic

Text Classification
Transformers
TensorBoard
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Model card Files
xet
 Metrics Community
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  1. README.md +36 -42
  2. best_model/model.safetensors +1 -1
  3. best_model/training_args.bin +1 -1
  4. model.safetensors +1 -1
  5. runs/Aug06_15-42-04_erc-hpc-comp031/events.out.tfevents.1786050127.erc-hpc-comp031.2302155.1 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -16,15 +16,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0151
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- - Macro F1: 0.4028
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- - Micro F1: 0.5675
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- - Macro Precision: 0.7364
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- - Macro Recall: 0.3091
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- - Micro Precision: 0.8374
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- - Micro Recall: 0.4291
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- - Exact Match Ratio: 0.1092
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- - Macro Roc Auc: 0.9269
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  ## Model description
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@@ -43,50 +43,44 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
 
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_steps: 0.1
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- - num_epochs: 200
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Macro Precision | Macro Recall | Micro Precision | Micro Recall | Exact Match Ratio | Macro Roc Auc |
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  |:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|:-----------------:|:-------------:|
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- | 0.0331 | 6.5789 | 500 | 0.0255 | 0.0000 | 0.0001 | 0.0061 | 0.0000 | 0.0260 | 0.0000 | 0.0058 | 0.5815 |
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- | 0.0286 | 13.1579 | 1000 | 0.0219 | 0.0321 | 0.0954 | 0.1715 | 0.0200 | 0.7864 | 0.0508 | 0.0115 | 0.7792 |
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- | 0.0255 | 19.7368 | 1500 | 0.0207 | 0.0498 | 0.1326 | 0.3072 | 0.0317 | 0.8760 | 0.0717 | 0.0210 | 0.8132 |
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- | 0.0242 | 26.3158 | 2000 | 0.0189 | 0.1174 | 0.2706 | 0.3581 | 0.0803 | 0.8779 | 0.1600 | 0.0560 | 0.8457 |
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- | 0.0224 | 32.8947 | 2500 | 0.0182 | 0.1439 | 0.3002 | 0.4783 | 0.0974 | 0.8678 | 0.1815 | 0.0611 | 0.8658 |
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- | 0.0218 | 39.4737 | 3000 | 0.0176 | 0.1759 | 0.3506 | 0.5072 | 0.1212 | 0.8672 | 0.2197 | 0.0660 | 0.8763 |
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- | 0.0208 | 46.0526 | 3500 | 0.0175 | 0.1726 | 0.3552 | 0.5286 | 0.1159 | 0.8733 | 0.2230 | 0.0668 | 0.8799 |
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- | 0.0195 | 52.6316 | 4000 | 0.0175 | 0.2054 | 0.3555 | 0.5903 | 0.1395 | 0.8757 | 0.2230 | 0.0667 | 0.8911 |
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- | 0.0190 | 59.2105 | 4500 | 0.0170 | 0.2605 | 0.4670 | 0.5664 | 0.1944 | 0.8264 | 0.3254 | 0.0844 | 0.8934 |
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- | 0.0181 | 65.7895 | 5000 | 0.0167 | 0.2875 | 0.4961 | 0.6030 | 0.2183 | 0.8140 | 0.3568 | 0.0828 | 0.8988 |
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- | 0.0176 | 72.3684 | 5500 | 0.0163 | 0.2886 | 0.4953 | 0.6718 | 0.2148 | 0.8333 | 0.3524 | 0.0879 | 0.9027 |
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- | 0.0175 | 78.9474 | 6000 | 0.0165 | 0.3284 | 0.5237 | 0.6525 | 0.2505 | 0.8133 | 0.3862 | 0.0918 | 0.9040 |
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- | 0.0165 | 85.5263 | 6500 | 0.0164 | 0.2734 | 0.4334 | 0.7028 | 0.1933 | 0.8704 | 0.2885 | 0.0836 | 0.9068 |
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- | 0.0160 | 92.1053 | 7000 | 0.0159 | 0.3092 | 0.5050 | 0.6930 | 0.2299 | 0.8381 | 0.3613 | 0.0891 | 0.9104 |
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- | 0.0153 | 98.6842 | 7500 | 0.0161 | 0.3255 | 0.5075 | 0.6818 | 0.2428 | 0.8315 | 0.3652 | 0.0853 | 0.9109 |
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- | 0.0152 | 105.2632 | 8000 | 0.0159 | 0.3157 | 0.5115 | 0.6832 | 0.2352 | 0.8368 | 0.3683 | 0.0903 | 0.9131 |
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- | 0.0144 | 111.8421 | 8500 | 0.0159 | 0.3502 | 0.5287 | 0.6968 | 0.2615 | 0.8258 | 0.3888 | 0.0919 | 0.9136 |
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- | 0.0142 | 118.4211 | 9000 | 0.0159 | 0.3545 | 0.5422 | 0.6768 | 0.2674 | 0.8219 | 0.4046 | 0.0987 | 0.9144 |
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- | 0.0138 | 125.0 | 9500 | 0.0158 | 0.3707 | 0.5545 | 0.7193 | 0.2826 | 0.8147 | 0.4202 | 0.0960 | 0.9167 |
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- | 0.0133 | 131.5789 | 10000 | 0.0158 | 0.3631 | 0.5532 | 0.6678 | 0.2773 | 0.8129 | 0.4193 | 0.0936 | 0.9169 |
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- | 0.0131 | 138.1579 | 10500 | 0.0158 | 0.3819 | 0.5506 | 0.6592 | 0.2915 | 0.8177 | 0.4150 | 0.0933 | 0.9172 |
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- | 0.0129 | 144.7368 | 11000 | 0.0158 | 0.3766 | 0.5443 | 0.7043 | 0.2859 | 0.8208 | 0.4071 | 0.0966 | 0.9179 |
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- | 0.0126 | 151.3158 | 11500 | 0.0157 | 0.3770 | 0.5439 | 0.6897 | 0.2843 | 0.8313 | 0.4042 | 0.0960 | 0.9183 |
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- | 0.0124 | 157.8947 | 12000 | 0.0158 | 0.3872 | 0.5572 | 0.6658 | 0.2982 | 0.8159 | 0.4230 | 0.0965 | 0.9181 |
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- | 0.0129 | 164.4737 | 12500 | 0.0157 | 0.3924 | 0.5575 | 0.6914 | 0.3008 | 0.8196 | 0.4224 | 0.0966 | 0.9184 |
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- | 0.0118 | 171.0526 | 13000 | 0.0157 | 0.3919 | 0.5616 | 0.6757 | 0.2997 | 0.8184 | 0.4275 | 0.0982 | 0.9197 |
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- | 0.0121 | 177.6316 | 13500 | 0.0157 | 0.3898 | 0.5625 | 0.7064 | 0.2987 | 0.8177 | 0.4286 | 0.0975 | 0.9197 |
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- | 0.0120 | 184.2105 | 14000 | 0.0157 | 0.3953 | 0.5648 | 0.6810 | 0.3049 | 0.8182 | 0.4313 | 0.0976 | 0.9196 |
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- | 0.0120 | 190.7895 | 14500 | 0.0157 | 0.3955 | 0.5674 | 0.7004 | 0.3052 | 0.8165 | 0.4348 | 0.0976 | 0.9198 |
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- | 0.0117 | 197.3684 | 15000 | 0.0157 | 0.3965 | 0.5679 | 0.6998 | 0.3059 | 0.8160 | 0.4355 | 0.0973 | 0.9198 |
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- | 0.0121 | 200.0 | 15200 | 0.0157 | 0.3961 | 0.5678 | 0.7001 | 0.3057 | 0.8162 | 0.4354 | 0.0976 | 0.9198 |
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  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.0161
20
+ - Macro F1: 0.4774
21
+ - Micro F1: 0.6157
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+ - Macro Precision: 0.7044
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+ - Macro Recall: 0.3876
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+ - Micro Precision: 0.7930
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+ - Micro Recall: 0.5033
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+ - Exact Match Ratio: 0.1179
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+ - Macro Roc Auc: 0.9285
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29
  ## Model description
30
 
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
46
+ - learning_rate: 5e-05
47
  - train_batch_size: 16
48
  - eval_batch_size: 16
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_steps: 0.1
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+ - num_epochs: 300
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57
  ### Training results
58
 
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  | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Macro Precision | Macro Recall | Micro Precision | Micro Recall | Exact Match Ratio | Macro Roc Auc |
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  |:-------------:|:--------:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|:-----------------:|:-------------:|
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+ | 0.0284 | 13.1579 | 500 | 0.0218 | 0.0295 | 0.1013 | 0.1733 | 0.0194 | 0.7882 | 0.0541 | 0.0097 | 0.7788 |
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+ | 0.0245 | 26.3158 | 1000 | 0.0193 | 0.0866 | 0.2206 | 0.3332 | 0.0575 | 0.8743 | 0.1262 | 0.0393 | 0.8454 |
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+ | 0.0220 | 39.4737 | 1500 | 0.0177 | 0.2134 | 0.4123 | 0.5269 | 0.1519 | 0.8249 | 0.2749 | 0.0708 | 0.8787 |
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+ | 0.0196 | 52.6316 | 2000 | 0.0173 | 0.2260 | 0.3989 | 0.6134 | 0.1577 | 0.8608 | 0.2596 | 0.0719 | 0.8917 |
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+ | 0.0181 | 65.7895 | 2500 | 0.0168 | 0.2959 | 0.5003 | 0.6590 | 0.2252 | 0.8068 | 0.3626 | 0.0832 | 0.9004 |
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+ | 0.0174 | 78.9474 | 3000 | 0.0165 | 0.3242 | 0.5262 | 0.6748 | 0.2462 | 0.8011 | 0.3918 | 0.0869 | 0.9059 |
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+ | 0.0162 | 92.1053 | 3500 | 0.0163 | 0.3373 | 0.5380 | 0.6913 | 0.2584 | 0.8024 | 0.4047 | 0.0883 | 0.9097 |
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+ | 0.0151 | 105.2632 | 4000 | 0.0162 | 0.3506 | 0.5519 | 0.6683 | 0.2737 | 0.8048 | 0.4199 | 0.0922 | 0.9130 |
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+ | 0.0140 | 118.4211 | 4500 | 0.0165 | 0.3816 | 0.5667 | 0.6721 | 0.3001 | 0.7953 | 0.4402 | 0.0933 | 0.9118 |
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+ | 0.0130 | 131.5789 | 5000 | 0.0165 | 0.3832 | 0.5772 | 0.6768 | 0.3069 | 0.7773 | 0.4590 | 0.0908 | 0.9156 |
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+ | 0.0123 | 144.7368 | 5500 | 0.0167 | 0.4083 | 0.5708 | 0.6855 | 0.3245 | 0.7845 | 0.4487 | 0.0925 | 0.9159 |
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+ | 0.0117 | 157.8947 | 6000 | 0.0166 | 0.4101 | 0.5904 | 0.6904 | 0.3310 | 0.7760 | 0.4764 | 0.0942 | 0.9164 |
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+ | 0.0110 | 171.0526 | 6500 | 0.0163 | 0.4059 | 0.5742 | 0.7061 | 0.3145 | 0.8047 | 0.4464 | 0.0951 | 0.9182 |
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+ | 0.0103 | 184.2105 | 7000 | 0.0168 | 0.4422 | 0.5878 | 0.6783 | 0.3585 | 0.7815 | 0.4710 | 0.0987 | 0.9194 |
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+ | 0.0097 | 197.3684 | 7500 | 0.0161 | 0.4262 | 0.5869 | 0.7098 | 0.3383 | 0.7951 | 0.4650 | 0.0973 | 0.9215 |
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+ | 0.0096 | 210.5263 | 8000 | 0.0169 | 0.4577 | 0.6076 | 0.6768 | 0.3725 | 0.7741 | 0.5 | 0.0982 | 0.9208 |
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+ | 0.0091 | 223.6842 | 8500 | 0.0166 | 0.4603 | 0.5994 | 0.6960 | 0.3704 | 0.7838 | 0.4853 | 0.0983 | 0.9220 |
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+ | 0.0088 | 236.8421 | 9000 | 0.0168 | 0.4557 | 0.6011 | 0.6903 | 0.3665 | 0.7842 | 0.4874 | 0.1018 | 0.9218 |
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+ | 0.0086 | 250.0 | 9500 | 0.0170 | 0.4673 | 0.6144 | 0.7100 | 0.3820 | 0.7726 | 0.5100 | 0.1029 | 0.9217 |
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+ | 0.0082 | 263.1579 | 10000 | 0.0165 | 0.4601 | 0.6057 | 0.6922 | 0.3692 | 0.7851 | 0.4930 | 0.1041 | 0.9231 |
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+ | 0.0083 | 276.3158 | 10500 | 0.0166 | 0.4669 | 0.6052 | 0.6946 | 0.3741 | 0.7875 | 0.4915 | 0.1031 | 0.9229 |
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+ | 0.0081 | 289.4737 | 11000 | 0.0168 | 0.4694 | 0.6096 | 0.6894 | 0.3794 | 0.7827 | 0.4992 | 0.1030 | 0.9228 |
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+ | 0.0080 | 300.0 | 11400 | 0.0167 | 0.4672 | 0.6075 | 0.6897 | 0.3762 | 0.7847 | 0.4956 | 0.1024 | 0.9229 |
 
 
 
 
 
 
 
 
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  ### Framework versions
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